Optimization of Patient Flow and Process for a Primary Care Clinic During the COVID-19 Pandemic; A Virtue Ethics Analysis of the Washington County Sheriff's Office's Implementation and Use of Amazon Rekognition

My technical work and STS research are connected primarily through the concept of systems analysis (SA), which is directly related to and used in the practice of systems engineering. SA has multiple definitions, but broadly it refers to a problem-solving technique that deconstructs a system’s components and examines how well those components perform together. The results of a SA provide valuable information to decision makers and stakeholders, which then allows them to manage, improve, or reshape the system in question. Despite the SA link between my two projects, my technical and STS research differ in the context and extent to which SA is applied. My technical work utilized a SA approach to understand and assess the processes in a primary care clinic (PCC) within the University of Virginia (UVA) Health System. In my STS research, I explored how one law enforcement system’s implementation and use of a facial recognition technology (FRT) software was unethical.

My technical capstone work involved conducting a SA of the University Physicians of Charlottesville (UPC) Clinic. Many PCCs, including UPC, are facing patient flow and throughput issues due to sub-optimal healthcare system practices and additional challenges precipitated by the COVID-19 pandemic. To execute our SA, my team and I applied a two-phased approach that combined qualitative observations of the Clinic with quantitative data analyses. Collecting observations allowed us to determine the current state of the Clinic, develop an ideal patient flow model, and identify valid metrics to use in our evaluation of the Clinic’s performance. In the data analysis phase, we used data from UVA’s electronic medical records system to inform our selection and facilitate our assessment of factors that affect the chosen metrics. The goal of the SA was to provide the Clinic’s stakeholders and other PCCs facing similar issues with insights into what the operations of a more efficient PCC might look like, as well as highlight internal and external factors that can impact how well PCCs function.

My STS research takes a morally-focused approach to SA. Rather than supplying technical system insights to a set of stakeholders, my STS work attempts to provide a normative ethical judgment of the actions the Washington County Sheriff’s Office (WCSO) took when implementing and using Amazon Rekognition’s FRT software. In my analysis, the WCSO is the collective moral actor of interest within the larger socio-technical law enforcement system that includes citizens, businesses, and other government organizations. To judge the morality of the WCSO’s actions, I employ the virtue ethics framework in conjunction with a set of moral virtues that are relevant to policing bodies. I make and support the argument that, in this case, the WCSO acted immorally by failing to practice the virtues of accountability, transparency, and equity. By gaining a better understanding the WCSO’s moral failures, my goal is to promote discussions about how FRT can be ethically applied within complex law enforcement systems.

Completing my capstone and STS research projects simultaneously enhanced the quality of both. My capstone work allowed me to gain practice in performing a traditional systems analysis that incorporated a diverse set of stakeholders and considerations. The experience I gained by analyzing an existing system gave me a greater appreciation for the complexities of the larger socio-technical system that the WCSO operates within, as well as the associated responsibility that the WCSO has to act morally. My STS research prompted me reflect on my own behavior as a systems engineer and emphasized to me how important it is to always practice moral virtues, particularly when doing technical work that can impact or influence others.

School of Engineering and Applied Science
Bachelor of Science in Systems Engineering
Technical Advisor: Robert Riggs
STS Advisor: Benjamin Laugelli
Technical Team Members: Alexandra Schmid, Bryce Huffman, Margaret Cusack, Sarah Saas, Wei Wu, Aram Bahrini, Kimberly Dowdell, Karen Measells